Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations7384
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory634.7 KiB
Average record size in memory88.0 B

Variable types

Numeric11

Alerts

AFDP is highly overall correlated with CDP and 6 other fieldsHigh correlation
AT is highly overall correlated with CO and 1 other fieldsHigh correlation
CDP is highly overall correlated with AFDP and 5 other fieldsHigh correlation
CO is highly overall correlated with AFDP and 6 other fieldsHigh correlation
GTEP is highly overall correlated with AFDP and 5 other fieldsHigh correlation
NOX is highly overall correlated with AFDP and 2 other fieldsHigh correlation
TAT is highly overall correlated with AFDP and 4 other fieldsHigh correlation
TEY is highly overall correlated with AFDP and 5 other fieldsHigh correlation
TIT is highly overall correlated with AFDP and 5 other fieldsHigh correlation

Reproduction

Analysis started2024-11-29 06:21:28.948226
Analysis finished2024-11-29 06:21:40.497898
Duration11.55 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

AT
Real number (ℝ)

High correlation 

Distinct6662
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.225259
Minimum-6.2348
Maximum37.103
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)0.8%
Memory size57.8 KiB
2024-11-29T09:21:40.585976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-6.2348
5-th percentile3.678625
Q111.07325
median17.4565
Q323.68475
95-th percentile29.8324
Maximum37.103
Range43.3378
Interquartile range (IQR)12.6115

Descriptive statistics

Standard deviation8.0957833
Coefficient of variation (CV)0.46999486
Kurtosis-0.68087668
Mean17.225259
Median Absolute Deviation (MAD)6.3075
Skewness-0.11825516
Sum127191.31
Variance65.541708
MonotonicityNot monotonic
2024-11-29T09:21:40.744635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.562 4
 
0.1%
10.925 4
 
0.1%
19.751 4
 
0.1%
19.409 4
 
0.1%
26.125 3
 
< 0.1%
10.859 3
 
< 0.1%
12.124 3
 
< 0.1%
12.952 3
 
< 0.1%
25.256 3
 
< 0.1%
24.829 3
 
< 0.1%
Other values (6652) 7350
99.5%
ValueCountFrequency (%)
-6.2348 1
< 0.1%
-6.0421 1
< 0.1%
-5.9793 1
< 0.1%
-5.9031 1
< 0.1%
-5.8956 1
< 0.1%
-5.8847 1
< 0.1%
-5.82 1
< 0.1%
-5.8189 1
< 0.1%
-5.785 1
< 0.1%
-5.7711 1
< 0.1%
ValueCountFrequency (%)
37.103 1
< 0.1%
37.098 1
< 0.1%
36.264 1
< 0.1%
35.822 1
< 0.1%
35.461 1
< 0.1%
35.406 1
< 0.1%
35.395 1
< 0.1%
35.21 1
< 0.1%
35.161 1
< 0.1%
35.045 1
< 0.1%

AP
Real number (ℝ)

Distinct440
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1014.5091
Minimum989.4
Maximum1036.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2024-11-29T09:21:40.884016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum989.4
5-th percentile1004.915
Q11009.675
median1014
Q31018.3
95-th percentile1027.7
Maximum1036.6
Range47.2
Interquartile range (IQR)8.625

Descriptive statistics

Standard deviation6.8954301
Coefficient of variation (CV)0.0067968144
Kurtosis0.55027296
Mean1014.5091
Median Absolute Deviation (MAD)4.3
Skewness0.39205247
Sum7491135.3
Variance47.546957
MonotonicityNot monotonic
2024-11-29T09:21:41.203212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1012.2 62
 
0.8%
1015.9 62
 
0.8%
1008 57
 
0.8%
1012.8 56
 
0.8%
1015.2 55
 
0.7%
1011.9 53
 
0.7%
1012.1 53
 
0.7%
1016 53
 
0.7%
1014.6 53
 
0.7%
1014.1 52
 
0.7%
Other values (430) 6828
92.5%
ValueCountFrequency (%)
989.4 1
< 0.1%
989.42 1
< 0.1%
989.69 1
< 0.1%
989.8 1
< 0.1%
990.17 1
< 0.1%
990.3 1
< 0.1%
990.64 1
< 0.1%
990.81 1
< 0.1%
991.26 1
< 0.1%
991.4 1
< 0.1%
ValueCountFrequency (%)
1036.6 1
 
< 0.1%
1036.5 2
< 0.1%
1036.4 2
< 0.1%
1036.3 4
0.1%
1036.2 1
 
< 0.1%
1036 1
 
< 0.1%
1035.8 3
< 0.1%
1035.7 2
< 0.1%
1035.6 2
< 0.1%
1035.5 2
< 0.1%

AH
Real number (ℝ)

Distinct6819
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.647464
Minimum24.085
Maximum96.666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2024-11-29T09:21:41.312884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum24.085
5-th percentile43.22525
Q159.44725
median70.952
Q379.65375
95-th percentile86.637
Maximum96.666
Range72.581
Interquartile range (IQR)20.2065

Descriptive statistics

Standard deviation13.541116
Coefficient of variation (CV)0.19725589
Kurtosis-0.4678483
Mean68.647464
Median Absolute Deviation (MAD)9.745
Skewness-0.54219798
Sum506892.88
Variance183.36183
MonotonicityNot monotonic
2024-11-29T09:21:41.426813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.581 4
 
0.1%
64.955 4
 
0.1%
77.921 3
 
< 0.1%
61.308 3
 
< 0.1%
71.004 3
 
< 0.1%
83.196 3
 
< 0.1%
75.158 3
 
< 0.1%
78.838 3
 
< 0.1%
84.926 3
 
< 0.1%
68.608 3
 
< 0.1%
Other values (6809) 7352
99.6%
ValueCountFrequency (%)
24.085 1
< 0.1%
24.666 1
< 0.1%
29.27 1
< 0.1%
29.475 1
< 0.1%
29.551 1
< 0.1%
29.952 1
< 0.1%
30.211 1
< 0.1%
30.292 1
< 0.1%
30.368 1
< 0.1%
30.39 1
< 0.1%
ValueCountFrequency (%)
96.666 1
< 0.1%
96.657 1
< 0.1%
96.198 1
< 0.1%
95.219 2
< 0.1%
94.547 1
< 0.1%
94.483 1
< 0.1%
94.333 1
< 0.1%
94.296 1
< 0.1%
94.2 1
< 0.1%
94.183 1
< 0.1%

AFDP
Real number (ℝ)

High correlation 

Distinct6194
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5989091
Minimum2.3688
Maximum5.2395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2024-11-29T09:21:41.539354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.3688
5-th percentile2.63423
Q13.1173
median3.5385
Q34.194825
95-th percentile4.49551
Maximum5.2395
Range2.8707
Interquartile range (IQR)1.077525

Descriptive statistics

Standard deviation0.61022578
Coefficient of variation (CV)0.16955854
Kurtosis-1.1596599
Mean3.5989091
Median Absolute Deviation (MAD)0.5188
Skewness0.0174803
Sum26574.344
Variance0.3723755
MonotonicityNot monotonic
2024-11-29T09:21:41.783750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3312 5
 
0.1%
4.268 4
 
0.1%
3.7818 4
 
0.1%
3.416 4
 
0.1%
3.3527 4
 
0.1%
3.0408 4
 
0.1%
4.2308 4
 
0.1%
3.0238 4
 
0.1%
4.3214 4
 
0.1%
4.3485 4
 
0.1%
Other values (6184) 7343
99.4%
ValueCountFrequency (%)
2.3688 1
< 0.1%
2.3735 1
< 0.1%
2.3781 1
< 0.1%
2.3822 1
< 0.1%
2.3863 1
< 0.1%
2.3872 1
< 0.1%
2.3885 1
< 0.1%
2.3937 1
< 0.1%
2.3938 1
< 0.1%
2.3944 1
< 0.1%
ValueCountFrequency (%)
5.2395 1
< 0.1%
5.2296 1
< 0.1%
5.1902 1
< 0.1%
5.1097 1
< 0.1%
5.0733 1
< 0.1%
5.0692 1
< 0.1%
4.996 1
< 0.1%
4.9534 1
< 0.1%
4.9016 1
< 0.1%
4.8633 1
< 0.1%

GTEP
Real number (ℝ)

High correlation 

Distinct5726
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.130149
Minimum17.698
Maximum40.716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2024-11-29T09:21:41.912443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.698
5-th percentile18.96345
Q123.147
median25.331
Q330.01825
95-th percentile33.215
Maximum40.716
Range23.018
Interquartile range (IQR)6.87125

Descriptive statistics

Standard deviation4.4737367
Coefficient of variation (CV)0.17120977
Kurtosis-0.84061249
Mean26.130149
Median Absolute Deviation (MAD)3.9355
Skewness0.10762951
Sum192945.02
Variance20.01432
MonotonicityNot monotonic
2024-11-29T09:21:42.038757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.191 5
 
0.1%
23.558 5
 
0.1%
24.592 5
 
0.1%
25.247 5
 
0.1%
24.149 5
 
0.1%
23.189 5
 
0.1%
23.495 5
 
0.1%
30.668 5
 
0.1%
30.362 5
 
0.1%
30.2 4
 
0.1%
Other values (5716) 7335
99.3%
ValueCountFrequency (%)
17.698 1
< 0.1%
17.719 1
< 0.1%
17.738 1
< 0.1%
17.741 1
< 0.1%
17.761 1
< 0.1%
17.826 1
< 0.1%
17.857 2
< 0.1%
17.862 1
< 0.1%
17.878 1
< 0.1%
17.913 1
< 0.1%
ValueCountFrequency (%)
40.716 1
< 0.1%
40.106 1
< 0.1%
39.37 1
< 0.1%
38.922 1
< 0.1%
38.362 1
< 0.1%
38.171 1
< 0.1%
38.051 1
< 0.1%
37.877 1
< 0.1%
37.873 1
< 0.1%
37.864 1
< 0.1%

TIT
Real number (ℝ)

High correlation 

Distinct730
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1078.9747
Minimum1016
Maximum1100.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2024-11-29T09:21:42.153605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1016
5-th percentile1043.1
Q11070.5
median1080.3
Q31099.9
95-th percentile1100
Maximum1100.4
Range84.4
Interquartile range (IQR)29.4

Descriptive statistics

Standard deviation19.762449
Coefficient of variation (CV)0.018315952
Kurtosis-0.44268554
Mean1078.9747
Median Absolute Deviation (MAD)18.9
Skewness-0.70352987
Sum7967149.1
Variance390.55439
MonotonicityNot monotonic
2024-11-29T09:21:42.300816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100 831
 
11.3%
1099.9 679
 
9.2%
1100.1 291
 
3.9%
1099.8 219
 
3.0%
1099.7 52
 
0.7%
1100.2 40
 
0.5%
1074.6 34
 
0.5%
1076.7 34
 
0.5%
1077 34
 
0.5%
1080.5 32
 
0.4%
Other values (720) 5138
69.6%
ValueCountFrequency (%)
1016 1
< 0.1%
1016.7 1
< 0.1%
1018 1
< 0.1%
1018.8 1
< 0.1%
1019 1
< 0.1%
1019.8 1
< 0.1%
1020.1 1
< 0.1%
1020.2 1
< 0.1%
1020.6 2
< 0.1%
1020.9 1
< 0.1%
ValueCountFrequency (%)
1100.4 1
 
< 0.1%
1100.3 6
 
0.1%
1100.2 40
 
0.5%
1100.1 291
 
3.9%
1100 831
11.3%
1099.9 679
9.2%
1099.8 219
 
3.0%
1099.7 52
 
0.7%
1099.6 31
 
0.4%
1099.5 17
 
0.2%

TAT
Real number (ℝ)

High correlation 

Distinct1583
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546.64248
Minimum516.04
Maximum550.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2024-11-29T09:21:42.442511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum516.04
5-th percentile533.6115
Q1544.7475
median549.72
Q3550.03
95-th percentile550.34
Maximum550.59
Range34.55
Interquartile range (IQR)5.2825

Descriptive statistics

Standard deviation5.4890662
Coefficient of variation (CV)0.010041419
Kurtosis3.4386909
Mean546.64248
Median Absolute Deviation (MAD)0.51
Skewness-1.9152835
Sum4036408.1
Variance30.129848
MonotonicityNot monotonic
2024-11-29T09:21:42.587959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
549.99 98
 
1.3%
550.01 95
 
1.3%
549.95 88
 
1.2%
549.98 85
 
1.2%
549.97 83
 
1.1%
549.94 82
 
1.1%
549.96 81
 
1.1%
550.07 81
 
1.1%
550.05 78
 
1.1%
549.88 76
 
1.0%
Other values (1573) 6537
88.5%
ValueCountFrequency (%)
516.04 1
< 0.1%
517.61 1
< 0.1%
517.72 1
< 0.1%
518.3 2
< 0.1%
519.5 1
< 0.1%
519.76 1
< 0.1%
519.89 1
< 0.1%
520 1
< 0.1%
520.24 1
< 0.1%
520.94 1
< 0.1%
ValueCountFrequency (%)
550.59 1
 
< 0.1%
550.53 1
 
< 0.1%
550.52 2
 
< 0.1%
550.51 3
 
< 0.1%
550.5 2
 
< 0.1%
550.49 5
 
0.1%
550.48 4
 
0.1%
550.47 13
0.2%
550.46 8
0.1%
550.45 15
0.2%

TEY
Real number (ℝ)

High correlation 

Distinct3640
Distinct (%)49.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.99338
Minimum100.02
Maximum179.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2024-11-29T09:21:42.715671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100.02
5-th percentile108.1015
Q1126.255
median131.6
Q3147.16
95-th percentile161.0485
Maximum179.5
Range79.48
Interquartile range (IQR)20.905

Descriptive statistics

Standard deviation16.179208
Coefficient of variation (CV)0.12074633
Kurtosis-0.59872234
Mean133.99338
Median Absolute Deviation (MAD)13.385
Skewness-0.054348625
Sum989407.12
Variance261.76678
MonotonicityNot monotonic
2024-11-29T09:21:42.863455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.71 16
 
0.2%
130.36 14
 
0.2%
130.29 13
 
0.2%
130.95 13
 
0.2%
130.09 13
 
0.2%
129.86 13
 
0.2%
131.03 13
 
0.2%
130.54 13
 
0.2%
129.89 12
 
0.2%
130.84 12
 
0.2%
Other values (3630) 7252
98.2%
ValueCountFrequency (%)
100.02 1
< 0.1%
100.04 1
< 0.1%
100.07 1
< 0.1%
100.2 2
< 0.1%
100.36 1
< 0.1%
100.38 1
< 0.1%
100.41 1
< 0.1%
100.47 1
< 0.1%
100.5 1
< 0.1%
100.51 1
< 0.1%
ValueCountFrequency (%)
179.5 1
< 0.1%
178.31 1
< 0.1%
177.91 1
< 0.1%
177.88 1
< 0.1%
177.49 1
< 0.1%
176.91 1
< 0.1%
176.71 1
< 0.1%
176.55 1
< 0.1%
176.35 1
< 0.1%
176.25 1
< 0.1%

CDP
Real number (ℝ)

High correlation 

Distinct3129
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.097025
Minimum9.8708
Maximum15.159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2024-11-29T09:21:42.999254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9.8708
5-th percentile10.30615
Q111.46575
median11.933
Q313.148
95-th percentile13.973
Maximum15.159
Range5.2882
Interquartile range (IQR)1.68225

Descriptive statistics

Standard deviation1.1366005
Coefficient of variation (CV)0.093957032
Kurtosis-0.86777046
Mean12.097025
Median Absolute Deviation (MAD)0.9935
Skewness0.058758852
Sum89324.431
Variance1.2918608
MonotonicityNot monotonic
2024-11-29T09:21:43.129369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.985 14
 
0.2%
11.708 12
 
0.2%
11.774 12
 
0.2%
11.664 11
 
0.1%
13.168 10
 
0.1%
11.825 10
 
0.1%
11.77 10
 
0.1%
13.212 10
 
0.1%
13.193 10
 
0.1%
13.19 10
 
0.1%
Other values (3119) 7275
98.5%
ValueCountFrequency (%)
9.8708 1
< 0.1%
9.9178 1
< 0.1%
9.9239 1
< 0.1%
9.9358 2
< 0.1%
9.9399 1
< 0.1%
9.9408 1
< 0.1%
9.9466 1
< 0.1%
9.9511 1
< 0.1%
9.9521 1
< 0.1%
9.9595 1
< 0.1%
ValueCountFrequency (%)
15.159 1
< 0.1%
15.083 1
< 0.1%
15.042 1
< 0.1%
15.039 1
< 0.1%
15.029 1
< 0.1%
14.945 1
< 0.1%
14.911 1
< 0.1%
14.9 1
< 0.1%
14.899 1
< 0.1%
14.891 1
< 0.1%

CO
Real number (ℝ)

High correlation 

Distinct6796
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1299855
Minimum0.2128
Maximum41.097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2024-11-29T09:21:43.242794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.2128
5-th percentile1.20243
Q11.808175
median2.5334
Q33.70255
95-th percentile7.082625
Maximum41.097
Range40.8842
Interquartile range (IQR)1.894375

Descriptive statistics

Standard deviation2.2349624
Coefficient of variation (CV)0.7140488
Kurtosis34.323788
Mean3.1299855
Median Absolute Deviation (MAD)0.87275
Skewness3.9470984
Sum23111.813
Variance4.995057
MonotonicityNot monotonic
2024-11-29T09:21:43.362768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8193 4
 
0.1%
2.8986 4
 
0.1%
1.8798 3
 
< 0.1%
2.1199 3
 
< 0.1%
1.6348 3
 
< 0.1%
3.0022 3
 
< 0.1%
2.1124 3
 
< 0.1%
1.6889 3
 
< 0.1%
1.9587 3
 
< 0.1%
3.7755 3
 
< 0.1%
Other values (6786) 7352
99.6%
ValueCountFrequency (%)
0.2128 1
< 0.1%
0.2129 1
< 0.1%
0.21299 1
< 0.1%
0.21313 1
< 0.1%
0.21316 1
< 0.1%
0.21333 1
< 0.1%
0.2135 1
< 0.1%
0.21452 1
< 0.1%
0.21836 1
< 0.1%
0.22142 1
< 0.1%
ValueCountFrequency (%)
41.097 1
< 0.1%
36.454 1
< 0.1%
31.869 1
< 0.1%
29.601 1
< 0.1%
29.084 1
< 0.1%
26.286 1
< 0.1%
24.239 1
< 0.1%
19.141 1
< 0.1%
19.139 1
< 0.1%
16.022 1
< 0.1%

NOX
Real number (ℝ)

High correlation 

Distinct6540
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.890509
Minimum25.905
Maximum119.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2024-11-29T09:21:43.479694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum25.905
5-th percentile47.4616
Q152.399
median56.8385
Q365.09325
95-th percentile81.5201
Maximum119.68
Range93.775
Interquartile range (IQR)12.69425

Descriptive statistics

Standard deviation11.132464
Coefficient of variation (CV)0.18588026
Kurtosis3.8419631
Mean59.890509
Median Absolute Deviation (MAD)5.5735
Skewness1.618716
Sum442231.52
Variance123.93175
MonotonicityNot monotonic
2024-11-29T09:21:43.588830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.757 4
 
0.1%
53.81 4
 
0.1%
54.401 4
 
0.1%
51.249 4
 
0.1%
56.081 4
 
0.1%
53.176 4
 
0.1%
57.195 3
 
< 0.1%
52.7 3
 
< 0.1%
56.567 3
 
< 0.1%
53.54 3
 
< 0.1%
Other values (6530) 7348
99.5%
ValueCountFrequency (%)
25.905 1
< 0.1%
35.598 1
< 0.1%
36.676 1
< 0.1%
39.556 1
< 0.1%
39.643 1
< 0.1%
39.829 1
< 0.1%
40.039 1
< 0.1%
40.297 1
< 0.1%
40.423 1
< 0.1%
40.509 1
< 0.1%
ValueCountFrequency (%)
119.68 1
< 0.1%
119.52 1
< 0.1%
119.49 1
< 0.1%
119.29 1
< 0.1%
118.27 1
< 0.1%
117.87 1
< 0.1%
117.86 1
< 0.1%
117.7 1
< 0.1%
117.27 1
< 0.1%
116.96 1
< 0.1%

Interactions

2024-11-29T09:21:39.294657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:29.214829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:30.142731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:31.562501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.509419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.367618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.341143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.228247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:36.165513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.262876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.231524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:39.366907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:29.284576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:30.215941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:31.651585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.575562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.436270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.413925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.298867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:36.242437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.339087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.322395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:39.451782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:29.360943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:30.289340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:31.746617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.650246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.507253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.487065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.381365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:36.326678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.417041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.414346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:39.547894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:29.462593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:30.383250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:31.835182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.734919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.589067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.568809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.474379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:36.416332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.501940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.547783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:39.631109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:29.534939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:30.478921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:31.915643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.812524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.797364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.674041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.568980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:36.500829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.589087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.633215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:39.709869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:29.604845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:30.559924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:31.988059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.884654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.869626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.750206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.658503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:36.582781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.667057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.716685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:39.786682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:29.681387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:30.636592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.069587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.960956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.945991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.820980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.739117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:36.665296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.749375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.805636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:39.883024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:29.764227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:31.234181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.157112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.048363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.027492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.906650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.828576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:36.906330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.843288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.901754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:39.987053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:29.851563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:31.329085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.238523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.133070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.114264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.993752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.917919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.002593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.941203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.999607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:40.083615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:29.925985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:31.404474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.329510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.201186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.186205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.068560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.996265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.088766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.026848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:39.094050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:40.168502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:30.010756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:31.490243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:32.430873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:33.286962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:34.269290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:35.154807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:36.086527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:37.182747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:38.122380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-29T09:21:39.199891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-29T09:21:43.755744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AFDPAHAPATCDPCOGTEPNOXTATTEYTIT
AFDP1.000-0.245-0.1340.4940.926-0.7920.850-0.562-0.5060.8840.933
AH-0.2451.0000.067-0.452-0.2500.223-0.3120.0190.069-0.217-0.287
AP-0.1340.0671.000-0.487-0.0250.247-0.0770.177-0.107-0.016-0.099
AT0.494-0.452-0.4871.0000.276-0.5620.244-0.608-0.0010.1820.376
CDP0.926-0.250-0.0250.2761.000-0.7370.940-0.410-0.5630.9810.972
CO-0.7920.2230.247-0.562-0.7371.000-0.6620.5960.235-0.692-0.771
GTEP0.850-0.312-0.0770.2440.940-0.6621.000-0.307-0.5360.9310.914
NOX-0.5620.0190.177-0.608-0.4100.596-0.3071.0000.109-0.327-0.430
TAT-0.5060.069-0.107-0.001-0.5630.235-0.5360.1091.000-0.562-0.511
TEY0.884-0.217-0.0160.1820.981-0.6920.931-0.327-0.5621.0000.958
TIT0.933-0.287-0.0990.3760.972-0.7710.914-0.430-0.5110.9581.000

Missing values

2024-11-29T09:21:40.277986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-29T09:21:40.436774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ATAPAHAFDPGTEPTITTATTEYCDPCONOX
01.953201020.184.9852.530420.1161048.7544.92116.2710.7997.4491113.250
11.219101020.187.5232.393718.5841045.5548.50109.1810.3476.4684112.020
20.949151022.278.3352.778922.2641068.8549.95125.8811.2563.633588.147
31.007501021.776.9422.817023.3581075.2549.63132.2111.7023.197287.078
41.285801021.676.7322.837723.4831076.2549.68133.5811.7372.383382.515
51.831901021.776.4112.841023.4951076.4549.92133.5811.8292.081281.193
62.074001022.075.9742.798122.9451073.7549.98131.5311.6872.252983.171
71.782401022.673.5352.832723.3371075.7550.01133.1811.7453.735085.749
81.593001023.272.8732.872923.6541078.5550.06135.3811.7723.639886.491
91.681901023.872.4412.905823.4631077.9550.12134.8611.7423.586686.328
ATAPAHAFDPGTEPTITTATTEYCDPCONOX
73745.78841029.285.3604.857033.6441100.0529.19165.4114.3221.795547.228
73754.35281029.287.1474.996034.0941100.0527.73167.0414.3781.761847.370
73763.76751029.089.6095.073333.4021099.8529.82165.7814.2051.935647.819
73773.42181028.791.0033.691122.8591073.5549.78129.8611.5493.673867.737
73783.37761028.592.7033.312820.2481057.6550.30117.4610.7835.348866.550
73793.62681028.593.2003.166119.0871037.0541.59109.0810.41110.993089.172
73804.16741028.694.0363.192319.0161037.6542.28108.7910.34411.144088.849
73815.48201028.595.2193.312818.8571038.0543.48107.8110.46211.414096.147
73825.88371028.794.2003.983123.5631076.9550.11131.4111.7713.313464.738
73836.03921028.894.5473.875222.5241067.9548.23125.4111.46211.9810109.240